Transcript Conservation vs development

Final lecture
• Development issues
• Conservation vs development in a
CBA framework
• The Kuznets curves paradigm
• Waste Prevention and policy
• Structural change and innovation:
sectors and geography
Conservation vs
development
Adapting CBA to uncertainty and
irreversibility: the QUASI OPTION
VALUE APPROACH
Krutilla, 1967
Simple example…
• A model was developed to cope with CBA under uncertainty
and Irreversibility
• Arrow, K. J., and A. C. Fisher (1974) ‘Environmental
preservation, uncertainty, and irreversibility.’ Quarterly Journal
of Economics 88, 312–319
• Conrad, J. M. (1980) ‘Quasi-option value and the expected
value of information.’ Quarterly Journal of Economics 95, 813–
820
• Dixit, A.K., and R.S. Pindyck (1994) Investment Under
Uncertainty (Princeton University Press)
• ‘to economists, the important question about irreversibility is
this: what are the implications for resource allocation?’
(Fisher. A.C.)
• V0 – NPV of preservation 0-10 years, Period 1
• V0=10
• V1 – NPV 10 + years, period 2
• But we are uncertain
• α, V1= Vhigh = 400
• 1-α, V1= Vlow = 20
• Say α=0.2
• Expected value (V1)= α400 + (1-α)20 = 96
• Value of preservation
• V0 + Expected value (V1)= 106
development
• D0 – value period 1
• D1 value period 2
• D0+D1 =40+80=120
• With V0=10
Standard rule
• We develop if
• D0+D1 > V0+E(V1)
• Here 120>106
Arrow Fisher Rule
• But….the logic is flawed
D0
D1
D1
V0
Flexibility of
conserving
V1
With Irreversibility
V1
HIGH!
Arrow Fisher Rule
• V0 + αVhigh + (1-α)D1
154-120: value of
waiting/flexibility
• V0 + αVhigh + (1-α)D1
• 10+80+64=154!
Is conservation better? (with uncertainty and
irreversibility)
• The value of waiting or quasi option value depends on
• D0 level
• Here D0 must be < 74
• D1 vs Vl/Vh
• D0>V0
• The interesting case is >0 but not excessive….
• Vl < D1 < Vh
• Even D could be subject to uncertainty
Development in the long run. The
environmental Kuznets curve
hypothesis
The IPAT identity
• A simple but useful way to start thinking about what drives the
sizes of the economy’s impacts on the environment.
• It can be formalised as the IPAT identity:
I  PAT
(2.6)
I: impact, measured as mass or volume
P: population size
A: per capita affluence, in currency units
T: technology, amount of the resource used or waste generated per unit production
The IPAT identity
• Measure impact in terms of mass
• Use GDP for national income.
• Then T is resource or waste per unit GDP.
Then for the resource extraction case, we have:
GDP Resource Use
I  P

P
GDP
A
T
(2.6)
Affluence and technology: the Environmental
Kuznets Curve (EKC)
• World Development Report 1992, subtitled
‘Development and the environment’, noted that:
• ‘The view that greater economic activity inevitably
hurts the environment is based on static
assumptions about technology, tastes and
environmental investments’.
The EKC hypothesis
e y
e   0 y  1 y
EKC – Environmental Kuznets Curve
2
(a)
e
e = y
y
(b)
e
e = 0y - 1y2
y
Figure 2.8 Environmental impact and income
Panayotou (1993)
• “At low levels of development both the quantity and intensity of
environmental degradation is limited to the impacts of subsistence
economic activity on the resource base and to limited quantities of
biodegradable wastes. As economic development accelerates with
the intensification of agriculture and other resource extraction and
the takeoff of industrialisation, the rates of resource depletion begin
to exceed the rates of resource regeneration, and waste generation
increases in quantity and toxicity. At higher levels of development,
structural change towards information-intensive industries and
services, coupled with increased environmental awareness,
enforcement of environmental regulations, better technology and
higher environmental expenditures, result in levelling off and
gradual decline of environmental degradation.”
• The EKC hypothesis is shortly that for many environmental impacts,
an inverted U-shaped relationships between per capita income and
pollution is documented.
• The concentration of a certain pollutant first increases with
income/production, reflecting a scale effect, more or less
proportional, then eventually starts to decrease, de-linking from
income even on an absolute basis.
• More specifically, the hypothesis predicts that the “environmental
income elasticity” decreases monotonically with income, and that it
eventually changes its sign from positive to negative, thus defining a
turning point for the inverted U-shaped relationship.
• It does not derive from a theoretical model, it is an intuitive
conceptual approach, inductive in nature..though some theoretical
explanations have emerged…
EKC and delinking
• Delinking may occur on a relative basis (the
elasticity of the environmental impact indicator
with respect to an economic driver is positive,
but less than unity) or on an absolute basis
(negative elasticity).
• The assessment of both de-linking processes can
be referred to the mostly applied research field
concerning Environmental Kuznets Curves (EKC).
• The hypothesis derives from the original analysis
of Kuznets on the relationship between income
level and income distribution
EKC motivations
• Supply side
• Technology driven by economic growth (profits and investments..)
• The share of cleaner activities in GDP increases with the scale of the economy
(scale + composition effects)
• As scarcity increases, market prices should reflect it..self-regulatory mechanism?
• Environmental policy more likely in a developed economy  economic and
political conditions needed
• Property right enforcement (policy issue)
• Demand side
• Environmental quality is a normal luxury good (as culture)..higher incomes mean
higher WTP for the environmental services..higher taxes are possible, new
markets are profitable..
• Preferences change as the society develops..the marginal value of consumption is
positive but decreasing
• Environmental costs are increasing even steeply…growth benefits
decreasing….even a simple marginal cost-benefit scheme may explain why
delinking may occur
Policy relevance
• The EKC evidence may support the idea that no policy is
needed…market forces and market dynamics are selfsufficient in inverting the income-environment link
• BUT the environmental impact may be higher than what is
defined as sustainable…policy efforts are needed to support
and correct markets..affecting the shape of the EKC
• Empirical evidence, which has mainly concerned air
emissions, is still ambiguous. Some pollutants show a turning
point, though it shared view that some critical externalities,
like CO2 and waste flows, are monotonically rising with
income. At best, relative de-linking may be occurring (Stern,
2004).
• Air quality indicators
• Local air quality (CO, sulfur, PM) seem to show an
inverted U-shape with income.
• Global pollutants either rise monotonically with
income or eventually present very high turning point
(not reached if not by US)
• * private/public goods as far as countries are
concerned..free riding on global commons  policy
needed
• Water indicators
• The turning point is generally higher
• EKC for some indicators (local issues)
• N shape? (Borghesi, 1999)
waste
• Empirical evidence on Delinking concerning environmental
waste indicators is probably the scarcest. Contributions
providing results for waste are rare. Cole et al. (1997) find no
evidence for an inverted U-shape EKC curve concerning
municipal waste
• See the Mazzanti-Zoboli paper (2005) on waste and
delinking…
• There is currently no evidence concerning de-linking with
respect to primary sources of waste in Europe (i.e. municipal
and packaging waste), which have been targeted by wasteoriented European Directives aimed at reducing diverse
environmental externalities associated to waste production
and disposal
Empirical status of the EKC
hypothesis
• If economic growth is generally good for the environment, then it
would seem that there is no need to curtail growth in the world
economy in order to protect the global environment.
• In recent years there have been a number of studies using
econometric techniques to test the EKC hypothesis.
• Two key questions:
1. Are the data generally consistent with the EKC hypothesis?
2. If the EKC hypothesis holds, does the implication that growth is good
for the global environment follow?
Some evidence
• Turning points 2003$/per capita (international studies)
•
•
•
•
•
•
•
CO2:
CO
Nitrates
Nitrogen oxide
Sulfur dioxide
Sulfur dioxide (trans)
Suspended particulates
37000-57000
16000
25-41000
25-29000
10000
12-13000
12-13000
Lack of clean water
Decline uniformly with increasing income
Lack of urban sanitation
Decline uniformly with increasing income
Ambient levels of suspended particulate
matter in urban areas
Conform to EKC
Urban concentrations of sulphur dioxide
Conform to EKC
Change in forest area between 1961 and
1986,
Do not depend on income.
Change in rate of deforestation between 1961
and 1986,
Do not depend on income.
Dissolved oxygen in rivers
River quality tends to worsen with increasing
income
Faecal coliforms in rivers
River quality tends to worsen with increasing
income
Municipal waste per capita
Rise with income
Carbon dioxide emissions per capita
Rise with income
Evidence
. Shafik and Bandyopadhyay summarise the
implications of their results by stating:
• “It is possible to ‘grow out of’ some
environmental problems, but there is nothing
automatic about doing so. Action tends to be
taken where there are generalised local costs
and substantial private and social benefits. ”
Implications of the EKC
Confirming an inverted U in per capita terms
does not necessarily imply that future growth
means lower environmental impact.
Stern et al (1996) projected economic growth
and population growth for every country with a
population in excess of 1 million. They then
used the relationship in Figure 2.10 to compute
each country’s SO2 emissions from 1990 to
2025, and added across countries – global
emissions grew from 383 million tonnes in 1990
to 1181 million tonnes in 2025.
Arrow et al (1995) concluded that
‘Economic growth is not a panacea for
environmental quality.....policies that promote
gross national product growth are not substitutes
for environmental policy’
Carbon dioxide
We already tried to focus on specific homogeneous
areas rather than OECD or full sample
G7
FRANCE
UK
FITTED VALUES (SWAMY)
CANADA
JAPAN
FITTED VALUES (FE)
GERMANY
USA
ITALY
FITTED VALUES (BAYES)
2
log(co2)
1,5
1
0,5
0
8
8,5
9
9,5
10
10,5
log(y)
Source: Mazzanti, Musolesi and Zoboli, 2010, Applied Economics
CANADA
JAPAN
9
9.4
9.6
log(GDP per capita)
9.8
9.2
10
9.4
9.6
log(GDP per capita)
9.8
10
8
log(CO2 per capita)
1.4
1.2
10
.6
1.6
.8
.8
.9
1
log(CO2 per capita)
1.9
1.8
9.5
NEW ZELAND
NORWAY
1.7
9
log(GDP per capita)
1.2
USA
8.5
1.1
9.2
1
9
.4
1.2
1.3
.6
.8
1
log(CO2 per capita)
1.6
1.5
1.4
1.4
1.6
log(CO2 per capita)
1.2
1.7
1.8
AUSTRALIA
9.4
9.6
9.8
log(GDP per capita)
10
10.2
9.2
9
9.5
log(GDP per capita)
10
EKC, CO2 diversity in long run trends, while
most studies focus on average coefficient
estimations (e.g. OECD)
9.3
9.4
9.5
log(GDP per capita)
9.6
9.7
NETHERLANDS
FINLAND
1.2
1
log(CO2 per capita)
1.2
1
1
.6
1.1
.8
1.1
1.2
1.3
log(CO2 per capita)
1.4
1.3
1.4
1.5
1.4
DENMARK
9
9
9.2
9.4
9.6
log(GDP per capita)
9.8
9.2
10
9.4
9.6
log(GDP per capita)
9.8
10
8.5
9
9.5
BELGIUM
1
.9
9.5
10
9
9.2
10
UK
1.45
9.8
1.4
1.3
1.2
9
log(GDP per capita)
9.4
9.6
log(GDP per capita)
1.35
log(CO2 per capita)
1.3
1.4
log(CO2 per capita)
1.5
1.4
1.3
1.2
1.1
8.5
9.2
1.45
1.5
1.6
GERMANY
SWEDEN
9
1.3
1.35
EU North
.9
1.25
1
1.1
log(CO2 per capita)
1.2
1.4
1.3
FRANCE
9
9
9.2
9.4
9.6
log(GDP per capita)
10
log(GDP per capita)
9.8
10
9.2
9.4
9.6
log(GDP per capita)
9.8
10
9.4
log(GDP per capita)
9.6
9.8
IRELAND
1.2
1.4
GREECE
1.2
1
log(CO2 per capita)
.8
.6
.2
.4
.8
.4
.6
.8
log(CO2 per capita)
1
1
PORTUGAL
8.5
.2
8
8.5
9
9.5
9
9.5
log(GDP per capita)
10
log(GDP per capita)
8
8.5
9
9.5
log(GDP per capita)
1.2
ITALY
.6
.8
log(CO2 per capita)
.8
.4
.6
.9
1
log(CO2 per capita)
1
1.1
1
1.2
SPAIN
1.2
AUSTRIA
.8
.4
8.5
9
9.5
log(GDP per capita)
8.5
9
9.5
10
log(GDP per capita)
EU South
8
8.5
9
log(GDP per capita)
9.5
10
10
Structural breaks
• Environmental Policy shocks
• Oil shocks
• Those can be captured by the time related component of the incomeenvironmental relationship..
• Disentangle income and time effects…
• Further look at separated effects by country
• Back to the heterogeneity issue
• Mazzanti, M. & Musolesi, A., 2013. The heterogeneity of carbon
Kuznets curves for advanced countries: comparing homogeneous,
heterogeneous and shrinkage/Bayesian estimators. Applied Economics,
45, pp.3827–3842.
• Musolesi, A. & Mazzanti, M., 2014. Nonlinearity, heterogeneity and
unobserved effects in the carbon dioxide emissions-economic
development relation for advanced countries. Studies in Nonlinear
Dynamics & Econometrics, 18(5), p.21.
0
.5
1
EU south
-.5
lco2pc
fitted_ramp93
fitted_ramp97
1960
1970
1980
year
fitted_step93
fitted_step97
1990
2000
.8
1
1.2
1.4
North America and Oceania
.6
lco2pc
fitted_ramp93
fitted_ramp97
1960
1970
1980
year
fitted_step93
fitted_step97
1990
2000
.8
.9
1
1.1
1.2
EU North
.7
lco2pc
fitted_ramp93
fitted_ramp97
1960
1970
1980
year
fitted_step93
fitted_step97
1990
2000
Waste (prevention)
Scenarios: MSW generation and
landfilling in the EU-27
Million tonnes waste
250
200
150
100
50
Municipal Solid Waste generation/landfilling
(million tonnes)
300
His torical
350
Projected
300
250
Estimated
recycling
Municipal W aste
generation
200
150
Incineration
100
Municipal W aste
landfilling
50
Estimated
landfill of BMW
Landfill
0
1980
1985
1990
1995
2000
Year
2005
2010
2015
2020
0
Note: Figures from 1980-2004 are data from Eurostat.
1980
1985
1990
1995
2000 BMW
2005
2010
2015
2020
Figures from 2005-2020
are
projections.
= biodegradable
municipal
waste.
Source:
EEA (2007).
Year
Waste: not less important, and related
to climate change
Million tonnes CO2-equivalents
150
0
Net GHG emissions
Direct emissions
Indirect emissions
-150
1980
1985
1990
1995
2000
2005
2010
Year

 Moving away from landfilling
2015
2020
et
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D nds
en
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Sw a r
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Be en
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G iu m
Lu erm
x e an
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bo
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Au rg
st
r
Fr ia
an
c
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-2
5
Sp
I
t
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n, y
20
Fi 03
nl
an
Ire d
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to rtu
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20
U
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Sl h R e ni
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C ia
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s
M
Li al t
th a
ua
n
G ia
re
ec
Po e
la
nd
N
%
Landfilling, incineration and
material recovery
(EEA, 2009)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Landfill
Calculated material recovery
Incineration w ith enery recovery
EEA (2009), Diverting waste from landfill
Other recovery operations
Waste Management composition in EEA
countries. Year 2009. Share of total disposal.
EU 27: MSW generated (Kg per
capita) and major relevant policies
Malta
Greece
Portugal
Austria
Denmark
Ireland
Latvia
Sweden
Cyprus
Italy
Finland
Luxembourg
France
Netherlands
Poland
Slovakia
Belgium
Spain
United Kingdom
Czech Republic
Germany
Hungary
Estonia
Slovenia
Lithuania
Waste generation growth rate
between 1995/2009. EEA
countries.
80.0%
60.0%
40.0%
20.0%
0.0%
-20.0%
Policies
Innovation
Direct effect
Waste
Performance
Number of patent application filed at the EPO. Specific
waste technologies, 3 year moving average. (1981=100).
Material Recycling on the right axe.
150
200
175
125
Patent trends
150
100
125
75
100
75
50
50
25
25
0
1981
0
1984
1987
Not classified
Fertilizers from waste
Material recycling
1990
1993
1996
1999
2002
2005
Solid waste collection
Incineration and energy recovery
2008
• D’Amato A., Mazzanti M. Montini A., 2013, Waste
Management in Spatial Environments, Routledge, London.
• .
• Mazzanti M. Montini A., 2009, Waste & Environmental Policy,
Routledge, London.
• Mazzanti M. Montini A. (2014), Clustering waste
performances. Spatial and socio economic effects in the Italian
environment, in Handbook of Waste Management (edited by
Tom Kinnaman)
• Nicolli F. Mazzanti M. (2011), Diverting waste: the role of
innovation, in OECD, Invention and transfer of environmental
technologies, Paris: OECD.
• Mazzanti M. Montini A. (2014), Clustering waste
performances. Spatial and socio economic effects in the Italian
environment, in Handbook of Waste Management (edited by
Tom Kinnaman)
• Policy, geography, institutions matter…
Geography and
sectors
Composition of the economy, innovation and structural
change
Table 3–CO2 and SOX emission intensity (kg x 1M€ of value added, increasing order)
Region
Trentino Alto Adige
Campania
Valle d’Aosta
Piedmonte
Lazio
Marche
Lombardy
Abruzzo
Veneto
Emilia Romagna
Tuscany
ITALY
Calabria
Umbria
Friuli Venezia Giulia
Basilicata
Liguria
Sicily
Molise
Sardinia
Puglia
69
CO2
136
141
153
185
204
206
209
258
267
270
278
301
307
342
353
430
472
547
689
824
971
Region
Trentino Alto Adige
Valle d’Aosta
Abruzzo
Campania
Lombardy
Lazio
Marche
Piedmonte
Calabria
Basilicata
Emilia Romagna
Molise
Veneto
ITALY
Tuscany
Umbria
Friuli Venezia Giulia
Puglia
Liguria
Sicily
Sardinia
Can we say something on the divers?
SOX
39
45
69
78
99
101
108
108
123
224
226
276
300
315
349
373
539
859
886
1,347
1,530
V.Costantini, M.Mazzanti, A.Montini - Environmental Performance and Regional Innovation Spillovers
Beyond income – within the
EKC again…
• Environmental performances are driven by
• structural features (ECONOMIC SPECIALIZATION)
• Innovation (EFFICIENCY)
Brown vs Green economy…
Manufacturing is heavier.. But more innovative…. Again the IPAT framework..
EU 20% (now 15%) non binding manufacturing target by 2020 (vs?) GHG
Targets 2020-2030
Advanced services oriented economy risk: low innovation, low growth and
wages…..(EEA, 2014)
Shift-Share: productive specialization (industry mix)
component
0.2
0.1
CO2
SOx
NOx
0.0
NMVOC
PM10
-0.1
-0.2
Note: Below zero values indicate positive performances
72
V.Costantini, M.Mazzanti, A.Montini - Environmental Performance and Regional Innovation Spillovers
Shift-Share: efficiency component
1.1
0.9
0.7
CO2
0.5
SOx
0.3
NOx
0.1
NMVOC
-0.1
PM10
-0.3
Note: Below zero values indicate positive performances
73
V.Costantini, M.Mazzanti, A.Montini - Environmental Performance and Regional Innovation Spillovers
• Innovation counter balances growth driven environmental
effects
• Innovation is jointly occuring with structural change
(recomposition of the economy + innovation diffusion + skill
development)
• Innovative sectors are often the ‘heavy’ sectors (those subject
to env policies)
• Innovation induced by energy saving actions (even without
policy)
• Innovation induced by policies which bear more on heavy sectors